This paper explores human behavior in virtual networked communities, specifically individuals or groups' potential and expressive capacity to respond to internal and external stimuli, with assortative matching as a typical example. A modeling approach based on Multi-Agent Reinforcement Learning (MARL) is proposed, adding a multi-head attention function to the A3C algorithm to enhance learning effectiveness. This approach simulates human behavior in certain scenarios through various environmental parameter settings and agent action strategies. In our experiment, reinforcement learning is employed to serve specific agents that learn from environment status and competitor behaviors, optimizing strategies to achieve better results. The simulation includes individual and group levels, displaying possible paths to forming competitive advantages. This modeling approach provides a means for further analysis of the evolutionary dynamics of human behavior, communities, and organizations in various socioeconomic issues.

翻译：本文探讨虚拟网络社区中的人类行为，具体聚焦于个体或群体对内外刺激的响应潜力与表达能力，并以分类匹配为典型案例。提出一种基于多智能体强化学习的建模方法，通过在A3C算法中引入多头注意力机制提升学习效果。该方法通过环境参数设置与智能体行动策略的多样化配置，模拟特定场景下的人类行为。实验采用强化学习为特定智能体服务，使其从环境状态与竞争对手行为中学习，优化策略以获得更优结果。仿真涵盖个体与群体层面，展示了形成竞争优势的潜在路径。该建模方法为深入分析人类行为、社区及组织在社会经济议题中的演化动态提供了研究手段。